{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Planar data classification with one hidden layer\n",
    "\n",
    "Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression. \n",
    "\n",
    "**You will learn how to:**\n",
    "- Implement a 2-class classification neural network with a single hidden layer\n",
    "- Use units with a non-linear activation function, such as tanh \n",
    "- Compute the cross entropy loss \n",
    "- Implement forward and backward propagation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Packages ##\n",
    "\n",
    "Let's first import all the packages that you will need during this assignment.\n",
    "- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n",
    "- [sklearn](http://scikit-learn.org/stable/) provides simple and efficient tools for data mining and data analysis. \n",
    "- [matplotlib](http://matplotlib.org) is a library for plotting graphs in Python.\n",
    "- testCases provides some test examples to assess the correctness of your functions\n",
    "- planar_utils provide various useful functions used in this assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Package imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from testCases import *\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "import sklearn.linear_model\n",
    "from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(1) # set a seed so that the results are consistent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Dataset ##\n",
    "\n",
    "First, let's get the dataset you will work on. The following code will load a \"flower\" 2-class dataset into variables `X` and `Y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, Y = load_planar_dataset() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the dataset using matplotlib. The data looks like a \"flower\" with some red (label y=0) and some blue (y=1) points. Your goal is to build a model to fit this data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the data:\n",
    "plt.scatter(X[0, :], X[1, :], c=Y[0, :], s=40, cmap=plt.cm.Spectral);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have:\n",
    "    - a numpy-array (matrix) X that contains your features (x1, x2)\n",
    "    - a numpy-array (vector) Y that contains your labels (red:0, blue:1).\n",
    "\n",
    "Lets first get a better sense of what our data is like. \n",
    "\n",
    "**Exercise**: How many training examples do you have? In addition, what is the `shape` of the variables `X` and `Y`? \n",
    "\n",
    "**Hint**: How do you get the shape of a numpy array? [(help)](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.shape.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 它这个数据是两行，400一行x，400一行y坐标，不是传统的shape是(数量，内容)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X is: (2, 400)\n",
      "The shape of Y is: (1, 400)\n",
      "I have m = 400 training examples!\n"
     ]
    }
   ],
   "source": [
    "### START CODE HERE ### (≈ 3 lines of code)\n",
    "shape_X = X.shape\n",
    "shape_Y = Y.shape\n",
    "m = shape_X[1]\n",
    "\n",
    "\n",
    "### END CODE HERE ###\n",
    "\n",
    "print ('The shape of X is: ' + str(shape_X))\n",
    "print ('The shape of Y is: ' + str(shape_Y))\n",
    "print ('I have m = %d training examples!' % (m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "       \n",
    "<table style=\"width:20%\">  \n",
    "  <tr>\n",
    "    <td>**shape of X**</td>\n",
    "    <td> (2, 400) </td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>**shape of Y**</td>\n",
    "    <td>(1, 400) </td> \n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>**m**</td>\n",
    "    <td> 400 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Simple Logistic Regression\n",
    "\n",
    "Before building a full neural network, lets first see how logistic regression performs on this problem. You can use sklearn's built-in functions to do that. Run the code below to train a logistic regression classifier on the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python37\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Train the logistic regression classifier\n",
    "clf = sklearn.linear_model.LogisticRegressionCV();\n",
    "clf.fit(X.T, Y[0, :].T);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now plot the decision boundary of these models. Run the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression: 47 % (percentage of correctly labelled datapoints)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the decision boundary for logistic regression\n",
    "plot_decision_boundary(lambda x: clf.predict(x), X, Y[0, :])\n",
    "plt.title(\"Logistic Regression\")\n",
    "\n",
    "# Print accuracy\n",
    "LR_predictions = clf.predict(X.T)\n",
    "print ('Accuracy of logistic regression: %d ' % float((np.dot(Y,LR_predictions) + np.dot(1-Y,1-LR_predictions))/float(Y.size)*100) +\n",
    "       '% ' + \"(percentage of correctly labelled datapoints)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**Accuracy**</td>\n",
    "    <td> 47% </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**: The dataset is not linearly separable, so logistic regression doesn't perform well. Hopefully a neural network will do better. Let's try this now! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Neural Network model\n",
    "\n",
    "Logistic regression did not work well on the \"flower dataset\". You are going to train a Neural Network with a single hidden layer.\n",
    "\n",
    "**Here is our model**:\n",
    "<img src=\"images/classification_kiank.png\" style=\"width:600px;height:300px;\">\n",
    "\n",
    "**Mathematically**:\n",
    "\n",
    "For one example $x^{(i)}$:\n",
    "$$z^{[1] (i)} =  W^{[1]} x^{(i)} + b^{[1] (i)}\\tag{1}$$ \n",
    "$$a^{[1] (i)} = \\tanh(z^{[1] (i)})\\tag{2}$$\n",
    "$$z^{[2] (i)} = W^{[2]} a^{[1] (i)} + b^{[2] (i)}\\tag{3}$$\n",
    "$$\\hat{y}^{(i)} = a^{[2] (i)} = \\sigma(z^{ [2] (i)})\\tag{4}$$\n",
    "$$y^{(i)}_{prediction} = \\begin{cases} 1 & \\mbox{if } a^{[2](i)} > 0.5 \\\\ 0 & \\mbox{otherwise } \\end{cases}\\tag{5}$$\n",
    "\n",
    "Given the predictions on all the examples, you can also compute the cost $J$ as follows: \n",
    "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large\\left(\\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right)  \\large  \\right) \\small \\tag{6}$$\n",
    "\n",
    "**Reminder**: The general methodology to build a Neural Network is to:\n",
    "    1. Define the neural network structure ( # of input units,  # of hidden units, etc). \n",
    "    2. Initialize the model's parameters\n",
    "    3. Loop:\n",
    "        - Implement forward propagation\n",
    "        - Compute loss\n",
    "        - Implement backward propagation to get the gradients\n",
    "        - Update parameters (gradient descent)\n",
    "\n",
    "You often build helper functions to compute steps 1-3 and then merge them into one function we call `nn_model()`. Once you've built `nn_model()` and learnt the right parameters, you can make predictions on new data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 - Defining the neural network structure ####\n",
    "\n",
    "**Exercise**: Define three variables:\n",
    "    - n_x: the size of the input layer\n",
    "    - n_h: the size of the hidden layer (set this to 4) \n",
    "    - n_y: the size of the output layer\n",
    "\n",
    "**Hint**: Use shapes of X and Y to find n_x and n_y. Also, hard code the hidden layer size to be 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: layer_sizes\n",
    "\n",
    "def layer_sizes(X, Y):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- input dataset of shape (input size, number of examples)\n",
    "    Y -- labels of shape (output size, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    n_x -- the size of the input layer\n",
    "    n_h -- the size of the hidden layer\n",
    "    n_y -- the size of the output layer\n",
    "    \"\"\"\n",
    "    ### START CODE HERE ### (≈ 3 lines of code)\n",
    "    n_x = X.shape[0]\n",
    "    n_h = 4\n",
    "    n_y = Y.shape[0]\n",
    "    ### END CODE HERE ###\n",
    "    return (n_x, n_h, n_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of the input layer is: n_x = 5\n",
      "The size of the hidden layer is: n_h = 4\n",
      "The size of the output layer is: n_y = 2\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess = layer_sizes_test_case()\n",
    "(n_x, n_h, n_y) = layer_sizes(X_assess, Y_assess)\n",
    "print(\"The size of the input layer is: n_x = \" + str(n_x))\n",
    "print(\"The size of the hidden layer is: n_h = \" + str(n_h))\n",
    "print(\"The size of the output layer is: n_y = \" + str(n_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output** (these are not the sizes you will use for your network, they are just used to assess the function you've just coded).\n",
    "\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**n_x**</td>\n",
    "    <td> 5 </td> \n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>**n_h**</td>\n",
    "    <td> 4 </td> \n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>**n_y**</td>\n",
    "    <td> 2 </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 - Initialize the model's parameters ####\n",
    "\n",
    "**Exercise**: Implement the function `initialize_parameters()`.\n",
    "\n",
    "**Instructions**:\n",
    "- Make sure your parameters' sizes are right. Refer to the neural network figure above if needed.\n",
    "- You will initialize the weights matrices with random values. \n",
    "    - Use: `np.random.randn(a,b) * 0.01` to randomly initialize a matrix of shape (a,b).\n",
    "- You will initialize the bias vectors as zeros. \n",
    "    - Use: `np.zeros((a,b))` to initialize a matrix of shape (a,b) with zeros."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 回去之后手算一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters\n",
    "\n",
    "def initialize_parameters(n_x, n_h, n_y):\n",
    "    \"\"\"\n",
    "    Argument:\n",
    "    n_x -- size of the input layer\n",
    "    n_h -- size of the hidden layer\n",
    "    n_y -- size of the output layer\n",
    "    \n",
    "    Returns:\n",
    "    params -- python dictionary containing your parameters:\n",
    "                    W1 -- weight matrix of shape (n_h, n_x)\n",
    "                    b1 -- bias vector of shape (n_h, 1)\n",
    "                    W2 -- weight matrix of shape (n_y, n_h)\n",
    "                    b2 -- bias vector of shape (n_y, 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(2) # we set up a seed so that your output matches ours although the initialization is random.\n",
    "    \n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = np.random.randn(n_h, n_x) * 0.01\n",
    "    b1 = np.zeros((n_h, 1))\n",
    "    W2 = np.random.randn(n_y, n_h) * 0.01\n",
    "    b2 = np.zeros((n_y, 1))\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    assert (W1.shape == (n_h, n_x))\n",
    "    assert (b1.shape == (n_h, 1))\n",
    "    assert (W2.shape == (n_y, n_h))\n",
    "    assert (b2.shape == (n_y, 1))\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-0.00416758 -0.00056267]\n",
      " [-0.02136196  0.01640271]\n",
      " [-0.01793436 -0.00841747]\n",
      " [ 0.00502881 -0.01245288]]\n",
      "b1 = [[0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]]\n",
      "W2 = [[-0.01057952 -0.00909008  0.00551454  0.02292208]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "n_x, n_h, n_y = initialize_parameters_test_case()\n",
    "\n",
    "parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:90%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.00416758 -0.00056267]\n",
    " [-0.02136196  0.01640271]\n",
    " [-0.01793436 -0.00841747]\n",
    " [ 0.00502881 -0.01245288]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ 0.]\n",
    " [ 0.]\n",
    " [ 0.]\n",
    " [ 0.]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-0.01057952 -0.00909008  0.00551454  0.02292208]]</td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 - The Loop ####\n",
    "\n",
    "**Question**: Implement `forward_propagation()`.\n",
    "\n",
    "**Instructions**:\n",
    "- Look above at the mathematical representation of your classifier.\n",
    "- You can use the function `sigmoid()`. It is built-in (imported) in the notebook.\n",
    "- You can use the function `np.tanh()`. It is part of the numpy library.\n",
    "- The steps you have to implement are:\n",
    "    1. Retrieve each parameter from the dictionary \"parameters\" (which is the output of `initialize_parameters()`) by using `parameters[\"..\"]`.\n",
    "    2. Implement Forward Propagation. Compute $Z^{[1]}, A^{[1]}, Z^{[2]}$ and $A^{[2]}$ (the vector of all your predictions on all the examples in the training set).\n",
    "- Values needed in the backpropagation are stored in \"`cache`\". The `cache` will be given as an input to the backpropagation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: forward_propagation\n",
    "\n",
    "def forward_propagation(X, parameters):\n",
    "    \"\"\"\n",
    "    Argument:\n",
    "    X -- input data of size (n_x, m)\n",
    "    parameters -- python dictionary containing your parameters (output of initialization function)\n",
    "    \n",
    "    Returns:\n",
    "    A2 -- The sigmoid output of the second activation\n",
    "    cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\"\n",
    "    \"\"\"\n",
    "    # Retrieve each parameter from the dictionary \"parameters\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = parameters['W1']\n",
    "    b1 = parameters['b1']\n",
    "    W2 = parameters['W2']\n",
    "    b2 = parameters['b2']\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Implement Forward Propagation to calculate A2 (probabilities)\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    Z1 = np.dot(W1, X) + b1\n",
    "    A1 = np.tanh(Z1)\n",
    "    Z2 = np.dot(W2, A1) + b2\n",
    "    A2 = sigmoid(Z2)\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    assert(A2.shape == (1, X.shape[1]))\n",
    "    \n",
    "    cache = {\"Z1\": Z1,\n",
    "             \"A1\": A1,\n",
    "             \"Z2\": Z2,\n",
    "             \"A2\": A2}\n",
    "    \n",
    "    return A2, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.0004997557777419913 -0.0004969633532317802 0.0004381874509591466 0.500109546852431\n",
      "(1, 3)\n",
      "(2, 3)\n"
     ]
    }
   ],
   "source": [
    "X_assess, parameters = forward_propagation_test_case()\n",
    "\n",
    "A2, cache = forward_propagation(X_assess, parameters)\n",
    "\n",
    "# Note: we use the mean here just to make sure that your output matches ours. \n",
    "print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2']))\n",
    "print(A2.shape)\n",
    "print(X_assess.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table style=\"width:55%\">\n",
    "  <tr>\n",
    "    <td> -0.000499755777742 -0.000496963353232 0.000438187450959 0.500109546852 </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have computed $A^{[2]}$ (in the Python variable \"`A2`\"), which contains $a^{[2](i)}$ for every example, you can compute the cost function as follows:\n",
    "\n",
    "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large{(} \\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large{)} \\small\\tag{13}$$\n",
    "\n",
    "**Exercise**: Implement `compute_cost()` to compute the value of the cost $J$.\n",
    "\n",
    "**Instructions**:\n",
    "- There are many ways to implement the cross-entropy loss. To help you, we give you how we would have implemented\n",
    "$- \\sum\\limits_{i=0}^{m}  y^{(i)}\\log(a^{[2](i)})$:\n",
    "```python\n",
    "logprobs = np.multiply(np.log(A2),Y)\n",
    "cost = - np.sum(logprobs)                # no need to use a for loop!\n",
    "```\n",
    "\n",
    "(you can use either `np.multiply()` and then `np.sum()` or directly `np.dot()`).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: compute_cost\n",
    "\n",
    "def compute_cost(A2, Y, parameters):\n",
    "    \"\"\"\n",
    "    Computes the cross-entropy cost given in equation (13)\n",
    "    \n",
    "    Arguments:\n",
    "    A2 -- The sigmoid output of the second activation, of shape (1, number of examples)\n",
    "    Y -- \"true\" labels vector of shape (1, number of examples)\n",
    "    parameters -- python dictionary containing your parameters W1, b1, W2 and b2\n",
    "    \n",
    "    Returns:\n",
    "    cost -- cross-entropy cost given equation (13)\n",
    "    \"\"\"\n",
    "    \n",
    "    m = Y.shape[1] # number of example\n",
    "\n",
    "    # Compute the cross-entropy cost\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    cost = -(np.sum(Y * np.log(A2) + (1 - Y) * np.log(1 - A2))) / m\n",
    "                    # no need to use a for loop!\n",
    "\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    cost = np.squeeze(cost)     # makes sure cost is the dimension we expect. \n",
    "                                # E.g., turns [[17]] into 17 \n",
    "    assert(isinstance(cost, float))\n",
    "    \n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cost = 0.6929198937761266\n"
     ]
    }
   ],
   "source": [
    "A2, Y_assess, parameters = compute_cost_test_case()\n",
    "\n",
    "print(\"cost = \" + str(compute_cost(A2, Y_assess, parameters)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**cost**</td>\n",
    "    <td> 0.692919893776 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the cache computed during forward propagation, you can now implement backward propagation.\n",
    "\n",
    "**Question**: Implement the function `backward_propagation()`.\n",
    "\n",
    "**Instructions**:\n",
    "Backpropagation is usually the hardest (most mathematical) part in deep learning. To help you, here again is the slide from the lecture on backpropagation. You'll want to use the six equations on the right of this slide, since you are building a vectorized implementation.  \n",
    "\n",
    "<img src=\"images/grad_summary.png\" style=\"width:600px;height:300px;\">\n",
    "\n",
    "<!--\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } = \\frac{1}{m} (a^{[2](i)} - y^{(i)})$\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial W_2 } = \\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } a^{[1] (i) T} $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial b_2 } = \\sum_i{\\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)}}}$\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)} } =  W_2^T \\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } * ( 1 - a^{[1] (i) 2}) $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial W_1 } = \\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)} }  X^T $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} _i }{ \\partial b_1 } = \\sum_i{\\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)}}}$\n",
    "\n",
    "- Note that $*$ denotes elementwise multiplication.\n",
    "- The notation you will use is common in deep learning coding:\n",
    "    - dW1 = $\\frac{\\partial \\mathcal{J} }{ \\partial W_1 }$\n",
    "    - db1 = $\\frac{\\partial \\mathcal{J} }{ \\partial b_1 }$\n",
    "    - dW2 = $\\frac{\\partial \\mathcal{J} }{ \\partial W_2 }$\n",
    "    - db2 = $\\frac{\\partial \\mathcal{J} }{ \\partial b_2 }$\n",
    "    \n",
    "!-->\n",
    "\n",
    "- Tips:\n",
    "    - To compute dZ1 you'll need to compute $g^{[1]'}(Z^{[1]})$. Since $g^{[1]}(.)$ is the tanh activation function, if $a = g^{[1]}(z)$ then $g^{[1]'}(z) = 1-a^2$. So you can compute \n",
    "    $g^{[1]'}(Z^{[1]})$ using `(1 - np.power(A1, 2))`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: backward_propagation\n",
    "\n",
    "def backward_propagation(parameters, cache, X, Y):\n",
    "    \"\"\"\n",
    "    Implement the backward propagation using the instructions above.\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing our parameters \n",
    "    cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\".\n",
    "    X -- input data of shape (2, number of examples)\n",
    "    Y -- \"true\" labels vector of shape (1, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    grads -- python dictionary containing your gradients with respect to different parameters\n",
    "    \"\"\"\n",
    "    m = X.shape[1]\n",
    "    \n",
    "    # First, retrieve W1 and W2 from the dictionary \"parameters\".\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    W1 = parameters['W1']\n",
    "    W2 = parameters['W2']\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "        \n",
    "    # Retrieve also A1 and A2 from dictionary \"cache\".\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A1 = cache['A1']\n",
    "    A2 = cache['A2']\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Backward propagation: calculate dW1, db1, dW2, db2. \n",
    "    ### START CODE HERE ### (≈ 6 lines of code, corresponding to 6 equations on slide above)\n",
    "    dZ2 = A2 - Y\n",
    "    dW2 = np.dot(dZ2, A1.T) / m\n",
    "    db2 = np.sum(dZ2, axis=1, keepdims=True) / m\n",
    "    # 可能错了，自己推导的，实验之后居然对了，我可真是个小机灵鬼\n",
    "    dZ1 = np.dot(W2.T, dZ2) * (1 - A1 ** 2)\n",
    "    dW1 = np.dot(dZ1, X.T) / m\n",
    "    db1 = np.sum(dZ1, axis=1, keepdims=True) / m\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    grads = {\"dW1\": dW1,\n",
    "             \"db1\": db1,\n",
    "             \"dW2\": dW2,\n",
    "             \"db2\": db2}\n",
    "    \n",
    "    return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dW1 = [[ 0.01018708 -0.00708701]\n",
      " [ 0.00873447 -0.0060768 ]\n",
      " [-0.00530847  0.00369379]\n",
      " [-0.02206365  0.01535126]]\n",
      "db1 = [[-0.00069728]\n",
      " [-0.00060606]\n",
      " [ 0.000364  ]\n",
      " [ 0.00151207]]\n",
      "dW2 = [[ 0.00363613  0.03153604  0.01162914 -0.01318316]]\n",
      "db2 = [[0.06589489]]\n"
     ]
    }
   ],
   "source": [
    "parameters, cache, X_assess, Y_assess = backward_propagation_test_case()\n",
    "\n",
    "grads = backward_propagation(parameters, cache, X_assess, Y_assess)\n",
    "print (\"dW1 = \"+ str(grads[\"dW1\"]))\n",
    "print (\"db1 = \"+ str(grads[\"db1\"]))\n",
    "print (\"dW2 = \"+ str(grads[\"dW2\"]))\n",
    "print (\"db2 = \"+ str(grads[\"db2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output**:\n",
    "\n",
    "\n",
    "\n",
    "<table style=\"width:80%\">\n",
    "  <tr>\n",
    "    <td>**dW1**</td>\n",
    "    <td> [[ 0.01018708 -0.00708701]\n",
    " [ 0.00873447 -0.0060768 ]\n",
    " [-0.00530847  0.00369379]\n",
    " [-0.02206365  0.01535126]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**db1**</td>\n",
    "    <td>  [[-0.00069728]\n",
    " [-0.00060606]\n",
    " [ 0.000364  ]\n",
    " [ 0.00151207]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**dW2**</td>\n",
    "    <td> [[ 0.00363613  0.03153604  0.01162914 -0.01318316]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**db2**</td>\n",
    "    <td> [[ 0.06589489]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question**: Implement the update rule. Use gradient descent. You have to use (dW1, db1, dW2, db2) in order to update (W1, b1, W2, b2).\n",
    "\n",
    "**General gradient descent rule**: $ \\theta = \\theta - \\alpha \\frac{\\partial J }{ \\partial \\theta }$ where $\\alpha$ is the learning rate and $\\theta$ represents a parameter.\n",
    "\n",
    "**Illustration**: The gradient descent algorithm with a good learning rate (converging) and a bad learning rate (diverging). Images courtesy of Adam Harley.\n",
    "\n",
    "<img src=\"images/sgd.gif\" style=\"width:400;height:400;\"> <img src=\"images/sgd_bad.gif\" style=\"width:400;height:400;\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: update_parameters\n",
    "\n",
    "def update_parameters(parameters, grads, learning_rate = 1.2):\n",
    "    \"\"\"\n",
    "    Updates parameters using the gradient descent update rule given above\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters \n",
    "    grads -- python dictionary containing your gradients \n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your updated parameters \n",
    "    \"\"\"\n",
    "    # Retrieve each parameter from the dictionary \"parameters\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = parameters['W1']\n",
    "    b1 = parameters['b1']\n",
    "    W2 = parameters['W2']\n",
    "    b2 = parameters['b2']\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Retrieve each gradient from the dictionary \"grads\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    dW1 = grads['dW1']\n",
    "    db1 = grads['db1']\n",
    "    dW2 = grads['dW2']\n",
    "    db2 = grads['db2']\n",
    "    \n",
    "    ## END CODE HERE ###\n",
    "    \n",
    "    # Update rule for each parameter\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = W1 - learning_rate * dW1\n",
    "    W2 = W2 - learning_rate * dW2\n",
    "    b1 = b1 - learning_rate * db1\n",
    "    b2 = b2 - learning_rate * db2\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-0.00643025  0.01936718]\n",
      " [-0.02410458  0.03978052]\n",
      " [-0.01653973 -0.02096177]\n",
      " [ 0.01046864 -0.05990141]]\n",
      "b1 = [[-1.02420756e-06]\n",
      " [ 1.27373948e-05]\n",
      " [ 8.32996807e-07]\n",
      " [-3.20136836e-06]]\n",
      "W2 = [[-0.01041081 -0.04463285  0.01758031  0.04747113]]\n",
      "b2 = [[0.00010457]]\n"
     ]
    }
   ],
   "source": [
    "parameters, grads = update_parameters_test_case()\n",
    "parameters = update_parameters(parameters, grads)\n",
    "\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "\n",
    "<table style=\"width:80%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.00643025  0.01936718]\n",
    " [-0.02410458  0.03978052]\n",
    " [-0.01653973 -0.02096177]\n",
    " [ 0.01046864 -0.05990141]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ -1.02420756e-06]\n",
    " [  1.27373948e-05]\n",
    " [  8.32996807e-07]\n",
    " [ -3.20136836e-06]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-0.01041081 -0.04463285  0.01758031  0.04747113]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.00010457]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 - Integrate parts 4.1, 4.2 and 4.3 in nn_model() ####\n",
    "\n",
    "**Question**: Build your neural network model in `nn_model()`.\n",
    "\n",
    "**Instructions**: The neural network model has to use the previous functions in the right order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: nn_model\n",
    "\n",
    "def nn_model(X, Y, n_h, num_iterations = 10000, print_cost=False):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- dataset of shape (2, number of examples)\n",
    "    Y -- labels of shape (1, number of examples)\n",
    "    n_h -- size of the hidden layer\n",
    "    num_iterations -- Number of iterations in gradient descent loop\n",
    "    print_cost -- if True, print the cost every 1000 iterations\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learnt by the model. They can then be used to predict.\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)\n",
    "    n_x = layer_sizes(X, Y)[0]\n",
    "    n_y = layer_sizes(X, Y)[2]\n",
    "    \n",
    "    # Initialize parameters, then retrieve W1, b1, W2, b2. Inputs: \"n_x, n_h, n_y\". Outputs = \"W1, b1, W2, b2, parameters\".\n",
    "    ### START CODE HERE ### (≈ 5 lines of code)\n",
    "    parameters = initialize_parameters(*layer_sizes(X, Y))\n",
    "    W1 = parameters['W1']\n",
    "    b1 = parameters['b1']\n",
    "    W2 = parameters['W2']\n",
    "    b2 = parameters['b2']\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Loop (gradient descent)\n",
    "\n",
    "    for i in range(0, num_iterations):\n",
    "         \n",
    "        ### START CODE HERE ### (≈ 4 lines of code)\n",
    "        # Forward propagation. Inputs: \"X, parameters\". Outputs: \"A2, cache\".\n",
    "        A2, cache = forward_propagation(X, parameters)\n",
    "        # Cost function. Inputs: \"A2, Y, parameters\". Outputs: \"cost\".\n",
    "        cost = compute_cost(A2, Y, parameters)\n",
    "        \n",
    "        # Backpropagation. Inputs: \"parameters, cache, X, Y\". Outputs: \"grads\".\n",
    "        grads = backward_propagation(parameters, cache, X, Y)\n",
    "        \n",
    "        # Gradient descent parameter update. Inputs: \"parameters, grads\". Outputs: \"parameters\".\n",
    "        parameters = update_parameters(parameters, grads)\n",
    "        \n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Print the cost every 1000 iterations\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python37\\lib\\site-packages\\ipykernel_launcher.py:20: RuntimeWarning: divide by zero encountered in log\n",
      "F:\\pandown\\吴恩达\\01.神经网络和深度学习\\3.第三周 浅层神经网络\\编程作业\\Planar data classification with one hidden layer\\planar_utils.py:34: RuntimeWarning: overflow encountered in exp\n",
      "  s = 1/(1+np.exp(-x))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-4.18493456  5.33221084]\n",
      " [-7.52989379  1.24306184]\n",
      " [-4.19295028  5.32632132]\n",
      " [ 7.5298362  -1.24309485]]\n",
      "b1 = [[ 2.32926684]\n",
      " [ 3.79459008]\n",
      " [ 2.33002374]\n",
      " [-3.79469043]]\n",
      "W2 = [[-6033.83672555 -6008.12980729 -6033.1009575   6008.06638591]]\n",
      "b2 = [[-52.6660737]]\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess = nn_model_test_case()\n",
    "\n",
    "parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=False)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:90%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-4.18494056  5.33220609]\n",
    " [-7.52989382  1.24306181]\n",
    " [-4.1929459   5.32632331]\n",
    " [ 7.52983719 -1.24309422]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ 2.32926819]\n",
    " [ 3.79458998]\n",
    " [ 2.33002577]\n",
    " [-3.79468846]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-6033.83672146 -6008.12980822 -6033.10095287  6008.06637269]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[-52.66607724]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Predictions\n",
    "\n",
    "**Question**: Use your model to predict by building predict().\n",
    "Use forward propagation to predict results.\n",
    "\n",
    "**Reminder**: predictions = $y_{prediction} = \\mathbb 1 \\text{{activation > 0.5}} = \\begin{cases}\n",
    "      1 & \\text{if}\\ activation > 0.5 \\\\\n",
    "      0 & \\text{otherwise}\n",
    "    \\end{cases}$  \n",
    "    \n",
    "As an example, if you would like to set the entries of a matrix X to 0 and 1 based on a threshold you would do: ```X_new = (X > threshold)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: predict\n",
    "\n",
    "def predict(parameters, X):\n",
    "    \"\"\"\n",
    "    Using the learned parameters, predicts a class for each example in X\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters \n",
    "    X -- input data of size (n_x, m)\n",
    "    \n",
    "    Returns\n",
    "    predictions -- vector of predictions of our model (red: 0 / blue: 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Computes probabilities using forward propagation, and classifies to 0/1 using 0.5 as the threshold.\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A2, cache = forward_propagation(X, parameters)\n",
    "    predictions = A2 > 0.5\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions mean = 0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "parameters, X_assess = predict_test_case()\n",
    "\n",
    "predictions = predict(parameters, X_assess)\n",
    "print(\"predictions mean = \" + str(np.mean(predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "\n",
    "<table style=\"width:40%\">\n",
    "  <tr>\n",
    "    <td>**predictions mean**</td>\n",
    "    <td> 0.666666666667 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is time to run the model and see how it performs on a planar dataset. Run the following code to test your model with a single hidden layer of $n_h$ hidden units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.693048\n",
      "Cost after iteration 1000: 0.288083\n",
      "Cost after iteration 2000: 0.254385\n",
      "Cost after iteration 3000: 0.233864\n",
      "Cost after iteration 4000: 0.226792\n",
      "Cost after iteration 5000: 0.222644\n",
      "Cost after iteration 6000: 0.219731\n",
      "Cost after iteration 7000: 0.217504\n",
      "Cost after iteration 8000: 0.219448\n",
      "Cost after iteration 9000: 0.218556\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Boundary for hidden layer size 4')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a model with a n_h-dimensional hidden layer\n",
    "parameters = nn_model(X, Y, n_h = 4, num_iterations = 10000, print_cost=True)\n",
    "\n",
    "# Plot the decision boundary\n",
    "plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y[0, :])\n",
    "plt.title(\"Decision Boundary for hidden layer size \" + str(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:40%\">\n",
    "  <tr>\n",
    "    <td>**Cost after iteration 9000**</td>\n",
    "    <td> 0.218607 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 90%\n"
     ]
    }
   ],
   "source": [
    "# Print accuracy\n",
    "predictions = predict(parameters, X)\n",
    "print ('Accuracy: %d' % float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100) + '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:15%\">\n",
    "  <tr>\n",
    "    <td>**Accuracy**</td>\n",
    "    <td> 90% </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy is really high compared to Logistic Regression. The model has learnt the leaf patterns of the flower! Neural networks are able to learn even highly non-linear decision boundaries, unlike logistic regression. \n",
    "\n",
    "Now, let's try out several hidden layer sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 - Tuning hidden layer size (optional/ungraded exercise) ###\n",
    "\n",
    "Run the following code. It may take 1-2 minutes. You will observe different behaviors of the model for various hidden layer sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for 1 hidden units: 90.5 %\n",
      "Accuracy for 2 hidden units: 90.5 %\n",
      "Accuracy for 3 hidden units: 90.5 %\n",
      "Accuracy for 4 hidden units: 90.5 %\n",
      "Accuracy for 5 hidden units: 90.5 %\n",
      "Accuracy for 10 hidden units: 90.5 %\n",
      "Accuracy for 20 hidden units: 90.5 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x2304 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This may take about 2 minutes to run\n",
    "\n",
    "plt.figure(figsize=(16, 32))\n",
    "hidden_layer_sizes = [1, 2, 3, 4, 5, 10, 20]\n",
    "for i, n_h in enumerate(hidden_layer_sizes):\n",
    "    plt.subplot(5, 2, i+1)\n",
    "    plt.title('Hidden Layer of size %d' % n_h)\n",
    "    parameters = nn_model(X, Y, n_h, num_iterations = 5000)\n",
    "    plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y[0, :])\n",
    "    predictions = predict(parameters, X)\n",
    "    accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)\n",
    "    print (\"Accuracy for {} hidden units: {} %\".format(n_h, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- The larger models (with more hidden units) are able to fit the training set better, until eventually the largest models overfit the data. \n",
    "- The best hidden layer size seems to be around n_h = 5. Indeed, a value around here seems to  fits the data well without also incurring noticable overfitting.\n",
    "- You will also learn later about regularization, which lets you use very large models (such as n_h = 50) without much overfitting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optional questions**:\n",
    "\n",
    "**Note**: Remember to submit the assignment but clicking the blue \"Submit Assignment\" button at the upper-right. \n",
    "\n",
    "Some optional/ungraded questions that you can explore if you wish: \n",
    "- What happens when you change the tanh activation for a sigmoid activation or a ReLU activation?\n",
    "- Play with the learning_rate. What happens?\n",
    "- What if we change the dataset? (See part 5 below!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**You've learnt to:**\n",
    "- Build a complete neural network with a hidden layer\n",
    "- Make a good use of a non-linear unit\n",
    "- Implemented forward propagation and backpropagation, and trained a neural network\n",
    "- See the impact of varying the hidden layer size, including overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice work! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Performance on other datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "If you want, you can rerun the whole notebook (minus the dataset part) for each of the following datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dataset' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-70-37211b4357f5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;31m### END CODE HERE ###\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'dataset' is not defined"
     ]
    }
   ],
   "source": [
    "# Datasets\n",
    "noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()\n",
    "\n",
    "datasets = {\"noisy_circles\": noisy_circles,\n",
    "            \"noisy_moons\": noisy_moons,\n",
    "            \"blobs\": blobs,\n",
    "            \"gaussian_quantiles\": gaussian_quantiles}\n",
    "\n",
    "### START CODE HERE ### (choose your dataset)\n",
    "\n",
    "### END CODE HERE ###\n",
    "\n",
    "X, Y = datasets[dataset]\n",
    "X, Y = X.T, Y.reshape(1, Y.shape[0])\n",
    "\n",
    "# make blobs binary\n",
    "if dataset == \"blobs\":\n",
    "    Y = Y%2\n",
    "\n",
    "# Visualize the data\n",
    "plt.scatter(X[0, :], X[1, :], c=Y[0, :], s=40, cmap=plt.cm.Spectral);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congrats on finishing this Programming Assignment!\n",
    "\n",
    "Reference:\n",
    "- http://scs.ryerson.ca/~aharley/neural-networks/\n",
    "- http://cs231n.github.io/neural-networks-case-study/"
   ]
  }
 ],
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